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            <td width="10%" class="headerItem">Current view:</td>
            <td width="35%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">src/caffe/layers</a> - bias_layer.cpp<span style="font-size: 80%;"> (source / <a href="bias_layer.cpp.func-sort-c.html">functions</a>)</span></td>
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            <td width="10%" class="headerCovTableHead">Total</td>
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            <td class="headerItem">Test:</td>
            <td class="headerValue">code analysis</td>
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            <td class="headerItem">Lines:</td>
            <td class="headerCovTableEntry">2</td>
            <td class="headerCovTableEntry">62</td>
            <td class="headerCovTableEntryLo">3.2 %</td>
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            <td class="headerItem">Date:</td>
            <td class="headerValue">2020-09-11 22:50:33</td>
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            <td class="headerItem">Functions:</td>
            <td class="headerCovTableEntry">2</td>
            <td class="headerCovTableEntry">16</td>
            <td class="headerCovTableEntryLo">12.5 %</td>
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            <td class="headerValueLeg">            Lines:
            <span class="coverLegendCov">hit</span>
            <span class="coverLegendNoCov">not hit</span>
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<pre class="sourceHeading">          Line data    Source code</pre>
<pre class="source">
<a name="1"><span class="lineNum">       1 </span>            : #include &lt;vector&gt;</a>
<span class="lineNum">       2 </span>            : 
<span class="lineNum">       3 </span>            : #include &quot;caffe/filler.hpp&quot;
<span class="lineNum">       4 </span>            : #include &quot;caffe/layers/bias_layer.hpp&quot;
<span class="lineNum">       5 </span>            : #include &quot;caffe/util/math_functions.hpp&quot;
<span class="lineNum">       6 </span>            : 
<span class="lineNum">       7 </span>            : namespace caffe {
<span class="lineNum">       8 </span>            : 
<span class="lineNum">       9 </span>            : template &lt;typename Dtype&gt;
<span class="lineNum">      10 </span><span class="lineNoCov">          0 : void BiasLayer&lt;Dtype&gt;::LayerSetUp(const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; bottom,</span>
<span class="lineNum">      11 </span>            :       const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; top) {
<span class="lineNum">      12 </span><span class="lineNoCov">          0 :   if (bottom.size() == 1 &amp;&amp; this-&gt;blobs_.size() &gt; 0) {</span>
<span class="lineNum">      13 </span><span class="lineNoCov">          0 :     LOG(INFO) &lt;&lt; &quot;Skipping parameter initialization&quot;;</span>
<span class="lineNum">      14 </span><span class="lineNoCov">          0 :   } else if (bottom.size() == 1) {</span>
<span class="lineNum">      15 </span>            :     // bias is a learned parameter; initialize it
<span class="lineNum">      16 </span>            :     const BiasParameter&amp; param = this-&gt;layer_param_.bias_param();
<span class="lineNum">      17 </span><span class="lineNoCov">          0 :     const int axis = bottom[0]-&gt;CanonicalAxisIndex(param.axis());</span>
<span class="lineNum">      18 </span>            :     const int num_axes = param.num_axes();
<span class="lineNum">      19 </span><span class="lineNoCov">          0 :     CHECK_GE(num_axes, -1) &lt;&lt; &quot;num_axes must be non-negative, &quot;</span>
<span class="lineNum">      20 </span>            :                            &lt;&lt; &quot;or -1 to extend to the end of bottom[0]&quot;;
<span class="lineNum">      21 </span><span class="lineNoCov">          0 :     if (num_axes &gt;= 0) {</span>
<span class="lineNum">      22 </span><span class="lineNoCov">          0 :       CHECK_GE(bottom[0]-&gt;num_axes(), axis + num_axes)</span>
<span class="lineNum">      23 </span>            :           &lt;&lt; &quot;bias blob's shape extends past bottom[0]'s shape when applied &quot;
<span class="lineNum">      24 </span>            :           &lt;&lt; &quot;starting with bottom[0] axis = &quot; &lt;&lt; axis;
<span class="lineNum">      25 </span>            :     }
<span class="lineNum">      26 </span><span class="lineNoCov">          0 :     this-&gt;blobs_.resize(1);</span>
<span class="lineNum">      27 </span>            :     const vector&lt;int&gt;::const_iterator&amp; shape_start =
<span class="lineNum">      28 </span><span class="lineNoCov">          0 :         bottom[0]-&gt;shape().begin() + axis;</span>
<span class="lineNum">      29 </span>            :     const vector&lt;int&gt;::const_iterator&amp; shape_end =
<span class="lineNum">      30 </span><span class="lineNoCov">          0 :         (num_axes == -1) ? bottom[0]-&gt;shape().end() : (shape_start + num_axes);</span>
<span class="lineNum">      31 </span><span class="lineNoCov">          0 :     vector&lt;int&gt; bias_shape(shape_start, shape_end);</span>
<span class="lineNum">      32 </span><span class="lineNoCov">          0 :     this-&gt;blobs_[0].reset(new Blob&lt;Dtype&gt;(bias_shape));</span>
<span class="lineNum">      33 </span><span class="lineNoCov">          0 :     shared_ptr&lt;Filler&lt;Dtype&gt; &gt; filler(GetFiller&lt;Dtype&gt;(param.filler()));</span>
<span class="lineNum">      34 </span><span class="lineNoCov">          0 :     filler-&gt;Fill(this-&gt;blobs_[0].get());</span>
<span class="lineNum">      35 </span>            :   }
<span class="lineNum">      36 </span><span class="lineNoCov">          0 :   this-&gt;param_propagate_down_.resize(this-&gt;blobs_.size(), true);</span>
<span class="lineNum">      37 </span><span class="lineNoCov">          0 : }</span>
<span class="lineNum">      38 </span>            : 
<span class="lineNum">      39 </span>            : template &lt;typename Dtype&gt;
<span class="lineNum">      40 </span><span class="lineNoCov">          0 : void BiasLayer&lt;Dtype&gt;::Reshape(const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; bottom,</span>
<span class="lineNum">      41 </span>            :       const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; top) {
<span class="lineNum">      42 </span>            :   const BiasParameter&amp; param = this-&gt;layer_param_.bias_param();
<span class="lineNum">      43 </span><span class="lineNoCov">          0 :   Blob&lt;Dtype&gt;* bias = (bottom.size() &gt; 1) ? bottom[1] : this-&gt;blobs_[0].get();</span>
<span class="lineNum">      44 </span>            :   // Always set axis == 0 in special case where bias is a scalar
<span class="lineNum">      45 </span>            :   // (num_axes == 0). Mathematically equivalent for any choice of axis, so the
<span class="lineNum">      46 </span>            :   // actual setting can be safely ignored; and computation is most efficient
<span class="lineNum">      47 </span>            :   // with axis == 0 and (therefore) outer_dim_ == 1.
<span class="lineNum">      48 </span><span class="lineNoCov">          0 :   const int axis = (bias-&gt;num_axes() == 0) ?</span>
<span class="lineNum">      49 </span><span class="lineNoCov">          0 :       0 : bottom[0]-&gt;CanonicalAxisIndex(param.axis());</span>
<span class="lineNum">      50 </span><span class="lineNoCov">          0 :   CHECK_GE(bottom[0]-&gt;num_axes(), axis + bias-&gt;num_axes())</span>
<span class="lineNum">      51 </span>            :       &lt;&lt; &quot;bias blob's shape extends past bottom[0]'s shape when applied &quot;
<span class="lineNum">      52 </span>            :       &lt;&lt; &quot;starting with bottom[0] axis = &quot; &lt;&lt; axis;
<span class="lineNum">      53 </span><span class="lineNoCov">          0 :   for (int i = 0; i &lt; bias-&gt;num_axes(); ++i) {</span>
<span class="lineNum">      54 </span><span class="lineNoCov">          0 :     CHECK_EQ(bottom[0]-&gt;shape(axis + i), bias-&gt;shape(i))</span>
<span class="lineNum">      55 </span><span class="lineNoCov">          0 :         &lt;&lt; &quot;dimension mismatch between bottom[0]-&gt;shape(&quot; &lt;&lt; axis + i</span>
<span class="lineNum">      56 </span><span class="lineNoCov">          0 :         &lt;&lt; &quot;) and bias-&gt;shape(&quot; &lt;&lt; i &lt;&lt; &quot;)&quot;;</span>
<span class="lineNum">      57 </span>            :   }
<span class="lineNum">      58 </span><span class="lineNoCov">          0 :   outer_dim_ = bottom[0]-&gt;count(0, axis);</span>
<span class="lineNum">      59 </span><span class="lineNoCov">          0 :   bias_dim_ = bias-&gt;count();</span>
<span class="lineNum">      60 </span><span class="lineNoCov">          0 :   inner_dim_ = bottom[0]-&gt;count(axis + bias-&gt;num_axes());</span>
<span class="lineNum">      61 </span><span class="lineNoCov">          0 :   dim_ = bias_dim_ * inner_dim_;</span>
<span class="lineNum">      62 </span><span class="lineNoCov">          0 :   if (bottom[0] != top[0]) {</span>
<span class="lineNum">      63 </span><span class="lineNoCov">          0 :     top[0]-&gt;ReshapeLike(*bottom[0]);</span>
<span class="lineNum">      64 </span>            :   }
<span class="lineNum">      65 </span><span class="lineNoCov">          0 :   bias_multiplier_.Reshape(vector&lt;int&gt;(1, inner_dim_));</span>
<span class="lineNum">      66 </span><span class="lineNoCov">          0 :   if (bias_multiplier_.cpu_data()[inner_dim_ - 1] != Dtype(1)) {</span>
<span class="lineNum">      67 </span><span class="lineNoCov">          0 :     caffe_set(inner_dim_, Dtype(1), bias_multiplier_.mutable_cpu_data());</span>
<span class="lineNum">      68 </span>            :   }
<span class="lineNum">      69 </span><span class="lineNoCov">          0 : }</span>
<span class="lineNum">      70 </span>            : 
<span class="lineNum">      71 </span>            : template &lt;typename Dtype&gt;
<span class="lineNum">      72 </span><span class="lineNoCov">          0 : void BiasLayer&lt;Dtype&gt;::Forward_cpu(const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; bottom,</span>
<span class="lineNum">      73 </span>            :       const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; top) {
<span class="lineNum">      74 </span>            :   const Dtype* bias_data =
<span class="lineNum">      75 </span><span class="lineNoCov">          0 :       ((bottom.size() &gt; 1) ? bottom[1] : this-&gt;blobs_[0].get())-&gt;cpu_data();</span>
<span class="lineNum">      76 </span><span class="lineNoCov">          0 :   Dtype* top_data = top[0]-&gt;mutable_cpu_data();</span>
<span class="lineNum">      77 </span><span class="lineNoCov">          0 :   if (bottom[0] != top[0]) {</span>
<span class="lineNum">      78 </span><span class="lineNoCov">          0 :     const Dtype* bottom_data = bottom[0]-&gt;cpu_data();</span>
<span class="lineNum">      79 </span><span class="lineNoCov">          0 :     caffe_copy(bottom[0]-&gt;count(), bottom_data, top_data);</span>
<span class="lineNum">      80 </span>            :   }
<span class="lineNum">      81 </span><span class="lineNoCov">          0 :   for (int n = 0; n &lt; outer_dim_; ++n) {</span>
<span class="lineNum">      82 </span><span class="lineNoCov">          0 :     caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, bias_dim_,</span>
<span class="lineNum">      83 </span>            :         inner_dim_, 1, Dtype(1), bias_data,
<span class="lineNum">      84 </span>            :         bias_multiplier_.cpu_data(), Dtype(1), top_data);
<span class="lineNum">      85 </span><span class="lineNoCov">          0 :     top_data += dim_;</span>
<span class="lineNum">      86 </span>            :   }
<span class="lineNum">      87 </span><span class="lineNoCov">          0 : }</span>
<span class="lineNum">      88 </span>            : 
<span class="lineNum">      89 </span>            : template &lt;typename Dtype&gt;
<span class="lineNum">      90 </span><span class="lineNoCov">          0 : void BiasLayer&lt;Dtype&gt;::Backward_cpu(const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; top,</span>
<span class="lineNum">      91 </span>            :       const vector&lt;bool&gt;&amp; propagate_down, const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; bottom) {
<span class="lineNum">      92 </span><span class="lineNoCov">          0 :   if (propagate_down[0] &amp;&amp; bottom[0] != top[0]) {</span>
<span class="lineNum">      93 </span><span class="lineNoCov">          0 :     const Dtype* top_diff = top[0]-&gt;cpu_diff();</span>
<span class="lineNum">      94 </span><span class="lineNoCov">          0 :     Dtype* bottom_diff = bottom[0]-&gt;mutable_cpu_diff();</span>
<span class="lineNum">      95 </span><span class="lineNoCov">          0 :     caffe_copy(bottom[0]-&gt;count(), top_diff, bottom_diff);</span>
<span class="lineNum">      96 </span>            :   }
<span class="lineNum">      97 </span>            :   // in-place, we don't need to do anything with the data diff
<span class="lineNum">      98 </span><span class="lineNoCov">          0 :   const bool bias_param = (bottom.size() == 1);</span>
<span class="lineNum">      99 </span><span class="lineNoCov">          0 :   if ((!bias_param &amp;&amp; propagate_down[1]) ||</span>
<span class="lineNum">     100 </span>            :       (bias_param &amp;&amp; this-&gt;param_propagate_down_[0])) {
<span class="lineNum">     101 </span><span class="lineNoCov">          0 :     const Dtype* top_diff = top[0]-&gt;cpu_diff();</span>
<span class="lineNum">     102 </span>            :     Dtype* bias_diff = (bias_param ? this-&gt;blobs_[0].get() : bottom[1])
<span class="lineNum">     103 </span><span class="lineNoCov">          0 :         -&gt;mutable_cpu_diff();</span>
<span class="lineNum">     104 </span>            :     bool accum = bias_param;
<span class="lineNum">     105 </span><span class="lineNoCov">          0 :     for (int n = 0; n &lt; outer_dim_; ++n) {</span>
<span class="lineNum">     106 </span><span class="lineNoCov">          0 :       caffe_cpu_gemv(CblasNoTrans, bias_dim_, inner_dim_, Dtype(1),</span>
<span class="lineNum">     107 </span>            :           top_diff, bias_multiplier_.cpu_data(), Dtype(accum), bias_diff);
<span class="lineNum">     108 </span><span class="lineNoCov">          0 :       top_diff += dim_;</span>
<span class="lineNum">     109 </span>            :       accum = true;
<span class="lineNum">     110 </span>            :     }
<span class="lineNum">     111 </span>            :   }
<span class="lineNum">     112 </span><span class="lineNoCov">          0 : }</span>
<a name="113"><span class="lineNum">     113 </span>            : </a>
<span class="lineNum">     114 </span>            : #ifdef CPU_ONLY
<span class="lineNum">     115 </span><span class="lineNoCov">          0 : STUB_GPU(BiasLayer);</span>
<span class="lineNum">     116 </span>            : #endif
<span class="lineNum">     117 </span>            : 
<span class="lineNum">     118 </span>            : INSTANTIATE_CLASS(BiasLayer);
<a name="119"><span class="lineNum">     119 </span><span class="lineCov">          3 : REGISTER_LAYER_CLASS(Bias);</span></a>
<span class="lineNum">     120 </span>            : 
<span class="lineNum">     121 </span><span class="lineCov">          3 : }  // namespace caffe</span>
</pre>
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